Decision making is an abstract concept that can generally be thought of as a stimulation/response process usually seen in the context of problem solving. The process is stimulated by a set of information, including for example a set of criteria, a specific question, or a set of factors which define an issue to be addressed. The relevance of each piece of information relating to the problem needs to be gauged, both individually and collectively and ultimately the decision or outcome is made by matching these inputs to rules, knowledge or experience pertinent to the matter in hand.
At a less abstract level, decision making may be thought of as a simple question/answer process whereby an almost infinite potential source of information may be analysed in order to match the question with an answer on an isomorphic (i.e. one-to-one basis). However, many decision making paradigms do not satisfy simple single valued isomorphism as there may be any number of competing variables which may influence or affect the outcome of the decision making process.
Further, the decision making process should conform to an accepted or pre-determined standard or “rule”. In an abstract sense, it is increasingly common that decisions are made based on what is known as a “best practise” approach. Such decision making processes may not be necessarily solely focussed on the determination of an empirical answer to a specific question. The process may also or entirely involve subjective answers relating to experience, intuition and instinct (articulated appropriately) which have, over time, been associated with specific criteria or variable patterns and/or values.
Such rules are frequently created and documented by authorities or bodies of experts, or via a meta-analysis of the pertinent body of knowledge. That is, the standards can be evidence-based and can be thought of as including empirical as well as experiential data.
Thus, the standards in effect describe the “rules” around which decisions should be made and are intended to cover all or most of the possible eventualities or variable patterns/values.
In everyday experience, we are often presented with a specific instance of these possible eventualities or a specific example of a pattern of variables with which a decision needs to be made. An example might be determining the probability of precipitation given specific data relating to the present weather. In this case, the eventualities may include variables relating to temperature, humidity, lapse rates and the like. The output of the decision making process may be a probability of precipitation within a set period.
In endeavouring to determine the “best” or optimal decision, it may not be practical to be presented with or have access to the full body of the relevant knowledge and expect to distil from it information relevant to the particular instance or scenario in question. Rather, an effective knowledge-based system should address the specific scenario, be responsive to a users input and provide a clear, relevant and focused decision or output based on the input criteria.
Computer systems provide an ideal environment in which to develop and model knowledge-based systems. Their abilities in relation to data capture and storage, along with rapid search capabilities and other data processing functions make them ideal vehicles for the development and implementation of decision making systems.
It is considered that the prior art solutions do not fully meet the requirements of a flexible decision making system for the following reasons. Prior art techniques are generally unable to provide the specificity and speed required. Such techniques generally use a subject/predicate approach or fuzzy logic, rather than an object based approach, to deliver the required information, and are reductionist in nature rather than attempting to support real world situations.
In addition prior art solutions do not capture a body of expert opinion and make it available so that a less experienced user will be presented with the expert's solution in response to given scenarios in a way that is entirely controllable and reproducible through the way the knowledge base is established and maintained.
Also, prior art architectures are not easily extensible. Such a characteristic is considered desirable in that it allows a variable range of situations or scenarios and a greater depth of information. Generally many prior art systems require that the decision making process and interface be an integral part of the computer program which requires the knowledge base to be itself integrated into the program.
In such models, the knowledge base is not managed in a natural language and is generally concealed from the user. This is particularly problematic when the knowledge and rules exist in a narrative format (e.g. Standard Operating Procedures, protocols etc). An individual with a working knowledge of the area can determine the scenario matches from the advice presented, but would struggle to interpret these as a set of logic based formulae.
To the applicants knowledge, there are no decision making systems which are built on open system principles, whereby any client program conforming to the architecture specification can interact with the knowledge base. The consequence of this is that the accessibility and usability of the system is severely limited. Finally, many prior art systems do not allow real time up updating of the knowledge base. These types of system tend to rely on distributing updates via email or CD ROM. Having the knowledge base reside on a remote server operating on a client/server basis from a central location overcomes these problems.
The applicant is aware of attempts in the past to develop knowledge-based systems. Most deal with methodologies for defining, capturing and storing the knowledge or rules, but are silent on how the stored knowledge may be returned in a real world, situation specific manner.
The Unified Modelling Language (UML) is a notation for Object Oriented Analysis and Design outlined by Booch, Rumbaugh and Jacobsen. This does not identify how stored information is returned in the manner addressed in the proposed solution.
Common Object Request Broker Architecture (CORBA) is an emerging open distributed object computing infrastructure being standardised by the Object Management Group (OMG). CORBA automates many common network programming tasks such as object registration, location and activation, request demultiplexing, framing and error-handling, etc. The CORBA ORB Architecture requires extensive processing time in searching the knowledge base.
In the medical area an example of this is Arden Syntax for Medical Language Modules which provides subject/predicate logic to address very narrowly defined situations, but has no inherent method for returning advice.
Another known technique includes the use of GLIF—the Guideline Interchange Format. This corresponds to a standard architecture for describing a guideline in a reproducible, understandable and shareable format. Further related material may be found in a project established by Stanford Medical Informatics at the University of Stanford, California, known as Protege. This system allows developers to build knowledge-based systems by selecting and modifying reusable problem-solving methods and epistemologies. This system corresponds to a suite of tools that generate domain-specific knowledge-acquisition tools and applications from the epistemologies.